NDLI: Approximate Inference in State-Space Models With Heavy-Tailed Noise

Content Provider	IEEE Xplore Digital Library
Author	Agamennoni, G. Nieto, J.I. Nebot, E.M.
Copyright Year	1991
Abstract	State-space models have been successfully applied across a wide range of problems ranging from system control to target tracking and autonomous navigation. Their ubiquity stems from their modeling flexibility, as well as the development of a battery of powerful algorithms for estimating the state variables. For multivariate models, the Gaussian noise assumption is predominant due its convenient computational properties. In some cases, anyhow, this assumption breaks down and no longer holds. We propose a novel approach to extending the applicability of this class of models to a wider range of noise distributions without losing the computational advantages of the associated algorithms. The estimation methods we develop parallel the Kalman filter and thus are readily implemented and inherit the same order of complexity. We derive all of the equations and algorithms from first principles. In order to validate the performance of our approach, we present specific instances of non-Gaussian state-space models and test their performance on experiments with synthetic and real data.
Sponsorship	IEEE Signal Processing Society
Starting Page	5024
Ending Page	5037
Page Count	14
File Size	2205030
File Format	PDF
ISSN	1053587X
Volume Number	60
Issue Number	10
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-10-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Noise Robustness State-space methods Signal processing algorithms Approximation methods Estimation Kalman filters sub-exponential noise Bayesian outlier detection heavy-tailed noise inverse Wishart distribution robust estimation robust Kalman filter state-space models student's $t$ distribution
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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